AI SDK - Voyage AI Provider

Introduction

The Voyage AI Provider is a provider for the AI SDK. It provides a simple interface to the Voyage AI API.

Installation

npm install voyage-ai-provider

# or

yarn add voyage-ai-provider

# or

pnpm add voyage-ai-provider

# or

bun add voyage-ai-provider

Configuration

The Voyage AI Provider requires an API key to be configured. You can obtain an API key by signing up at Voyage AI.

add the following to your .env file:

VOYAGE_API_KEY=your-api-key

Usage

Text Embedding

import { voyage } from 'voyage-ai-provider';
import { embedMany } from 'ai';

const embeddingModel = voyage.textEmbeddingModel('voyage-3-lite');

export const generateEmbeddings = async (
  value: string,
): Promise<Array<{ embedding: number[]; content: string }>> => {
  // Generate chunks from the input value
  const chunks = value.split('\n');

  // Optional: You can also split the input value by comma
  // const chunks = value.split('.');

  // Or you can use LLM to generate chunks(summarize) from the input value

  const { embeddings } = await embedMany({
    model: embeddingModel,
    values: chunks,
  });
  return embeddings.map((e, i) => ({ content: chunks[i], embedding: e }));
};

How to pass additional settings to the model

The settings object should contain the settings you want to add to the model. You can find the available settings for the model in the Voyage API documentation: https://docs.voyageai.com/reference/embeddings-api

const voyage = createVoyage({
  apiKey: process.env.VOYAGE_API_KEY,
});

// Initialize the embedding model
const embeddingModel = voyage.textEmbeddingModel(
  'voyage-3-lite',
  // adding settings
  {
    inputType: 'document',
    outputDimension: '1024', // the new model voyage-code-3, voyage-3-large has 4 different output dimensions: 256, 512, 1024 (default), 2048
    outputDtype: 'float',
  },
);

Image & Multi-modal Embedding

Multimodal and image embeddings both use the voyage-multimodal-3 model and the same /multimodalembeddings endpoint. Following the AI SDK convention (and the official providers such as Google), the embed/embedMany values array holds the text for each embedding, and any non-text content (images) is passed via providerOptions.voyage.content.

content is an array aligned to values by index: content[i] are the extra parts merged with the text in values[i]. Its length must equal values.length. Use null for entries that are text-only. For an image-only embedding, pass an empty string ('') for that value.

Each content part is one of:

• { type: 'text', text: string }
• { type: 'image_url', image_url: string }
• { type: 'image_base64', image_base64: string }

Example 1: A single image per embedding (image-only)

import {
  voyage,
  type VoyageMultimodalEmbeddingOptions,
} from 'voyage-ai-provider';
import { embedMany } from 'ai';

const imageModel = voyage.imageEmbeddingModel('voyage-multimodal-3');

const { embeddings } = await embedMany({
  model: imageModel,
  values: ['', ''], // one empty string per image-only embedding
  providerOptions: {
    voyage: {
      content: [
        [
          {
            type: 'image_url',
            image_url:
              'https://raw.githubusercontent.com/voyage-ai/voyage-multimodal-3/refs/heads/main/images/banana_200_x_200.jpg',
          },
        ],
        [
          {
            type: 'image_base64',
            image_base64: 'data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAA...',
          },
        ],
      ],
    } satisfies VoyageMultimodalEmbeddingOptions,
  },
});

Example 2: Multiple images in a single embedding

import {
  voyage,
  type VoyageMultimodalEmbeddingOptions,
} from 'voyage-ai-provider';
import { embedMany } from 'ai';

const imageModel = voyage.imageEmbeddingModel('voyage-multimodal-3');

const { embeddings } = await embedMany({
  model: imageModel,
  values: [''],
  providerOptions: {
    voyage: {
      content: [
        [
          {
            type: 'image_url',
            image_url:
              'https://raw.githubusercontent.com/voyage-ai/voyage-multimodal-3/refs/heads/main/images/banana_200_x_200.jpg',
          },
          {
            type: 'image_base64',
            image_base64: 'data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAA...',
          },
        ],
      ],
    } satisfies VoyageMultimodalEmbeddingOptions,
  },
});

Example 3: Text and images combined per embedding

import {
  voyage,
  type VoyageMultimodalEmbeddingOptions,
} from 'voyage-ai-provider';
import { embedMany } from 'ai';

const multimodalModel = voyage.multimodalEmbeddingModel('voyage-multimodal-3');

const { embeddings } = await embedMany({
  model: multimodalModel,
  values: ['This is a banana', 'This is a coding test'],
  providerOptions: {
    voyage: {
      content: [
        [
          {
            type: 'image_url',
            image_url:
              'https://raw.githubusercontent.com/voyage-ai/voyage-multimodal-3/refs/heads/main/images/banana_200_x_200.jpg',
          },
        ],
        [
          {
            type: 'image_base64',
            image_base64: 'data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAA...',
          },
        ],
      ],
    } satisfies VoyageMultimodalEmbeddingOptions,
  },
});

[!TIP] If you are getting an error for an image URL not found, convert the image to base64 and pass it as an image_base64 part instead. The value should be a Base64-encoded image in the data URL format data:[<mediatype>];base64,<data>. Currently supported mediatypes are: image/png, image/jpeg, image/webp, and image/gif.

[!NOTE] The following constraints apply to the values list: The list must not contain more than 1,000 values. Each image must not contain more than 16 million pixels or be larger than 20 MB in size. With every 560 pixels of an image being counted as a token, each input in the list must not exceed 32,000 tokens, and the total number of tokens across all inputs must not exceed 320,000.

Voyage embedding models:

| Model | Context Length (tokens) | Embedding Dimension | | --------------------- | ----------------------- | ------------------------------ | | voyage-4-large | 32,000 | 1024 (default), 256, 512, 2048 | | voyage-4 | 32,000 | 1024 (default), 256, 512, 2048 | | voyage-4-lite | 32,000 | 1024 (default), 256, 512, 2048 | | voyage-code-3 | 32,000 | 1024 (default), 256, 512, 2048 | | voyage-finance-2 | 32,000 | 1024 | | voyage-law-2 | 16,000 | 1024 | | voyage-code-2 | 16,000 | 1536 | | voyage-3-large | 32,000 | 1024 (default), 256, 512, 2048 | | voyage-3.5 | 32,000 | 1024 (default), 256, 512, 2048 | | voyage-3.5-lite | 32,000 | 1024 (default), 256, 512, 2048 | | voyage-3 | 32,000 | 1024 | | voyage-3-lite | 32,000 | 512 | | voyage-multilingual-2 | 32,000 | 1024 |

[!WARNING] The older models are deprecated and will be removed in the future. Use the latest models instead. https://docs.voyageai.com/docs/embeddings

Multi-modal Embedding Models

| Model | Context Length (tokens) | Embedding Dimension | | --------------------- | ----------------------- | ------------------------------ | | voyage-multimodal-3.5 | 32,000 | 1024 (default), 256, 512, 2048 | | voyage-multimodal-3 | 32,000 | 1024 |

Reranking

Reranking helps improve search results by reordering documents based on their relevance to a query.

import { voyage } from 'voyage-ai-provider';
import { rerank } from 'ai';

const rerankingModel = voyage.reranking('rerank-2.5');

const result = await rerank({
  model: rerankingModel,
  query: 'talk about rain',
  documents: [
    'sunny day at the beach',
    'rainy day in the city',
    'snowy mountain peak',
  ],
  topN: 2,
});

How to pass additional settings to the reranking model

The settings object should contain the settings you want to add to the model. You can find the available settings for the model in the Voyage API documentation: https://docs.voyageai.com/reference/reranker-api

import { voyage, type VoyageRerankingOptions } from 'voyage-ai-provider';
import { rerank } from 'ai';

const rerankingModel = voyage.reranking('rerank-2.5');

const result = await rerank({
  model: rerankingModel,
  query: 'talk about rain',
  documents: [
    'sunny day at the beach',
    'rainy day in the city',
    'snowy mountain peak',
  ],
  topN: 2,
  providerOptions: {
    voyage: {
      returnDocuments: true, // Return documents in the response
      truncation: true, // Truncate inputs to fit context length
    } satisfies VoyageRerankingOptions,
  },
});

[!NOTE] The following constraints apply to reranking:

• Query token limits: rerank-2.5 and rerank-2.5-lite (8,000), rerank-2 (4,000), rerank-2-lite and rerank-1 (2,000), rerank-lite-1 (1,000)
• Query + document token limits: rerank-2.5 and rerank-2.5-lite (32,000), rerank-2 (16,000), rerank-2-lite and rerank-1 (8,000), rerank-lite-1 (4,000)
• If truncation is set to false, an error will be raised when these limits are exceeded

Voyage Reranking Models

| Model | Query Token Limit | Query + Document Token Limit | | --------------- | ----------------- | ---------------------------- | | rerank-2.5 | 8,000 | 32,000 | | rerank-2.5-lite | 8,000 | 32,000 | | rerank-2 | 4,000 | 16,000 | | rerank-2-lite | 2,000 | 8,000 | | rerank-1 | 2,000 | 8,000 | | rerank-lite-1 | 1,000 | 4,000 |

[!TIP] Use rerank-2.5 or rerank-2.5-lite for the best performance and accuracy. Older models (rerank-2, rerank-2-lite, rerank-1, rerank-lite-1) are available but may have lower performance. https://docs.voyageai.com/docs/reranker

Authors

• patelvivekdev